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  • Book Overview & Buying Python High Performance, Second Edition
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Python High Performance, Second Edition

Python High Performance, Second Edition - Second Edition

By : Dr. Gabriele Lanaro
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Python High Performance, Second Edition

Python High Performance, Second Edition

4 (2)
By: Dr. Gabriele Lanaro

Overview of this book

Python is a versatile language that has found applications in many industries. The clean syntax, rich standard library, and vast selection of third-party libraries make Python a wildly popular language. Python High Performance is a practical guide that shows how to leverage the power of both native and third-party Python libraries to build robust applications. The book explains how to use various profilers to find performance bottlenecks and apply the correct algorithm to fix them. The reader will learn how to effectively use NumPy and Cython to speed up numerical code. The book explains concepts of concurrent programming and how to implement robust and responsive applications using Reactive programming. Readers will learn how to write code for parallel architectures using Tensorflow and Theano, and use a cluster of computers for large-scale computations using technologies such as Dask and PySpark. By the end of the book, readers will have learned to achieve performance and scale from their Python applications.
Table of Contents (10 chapters)
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The PyPy project

PyPy is a very ambitious project at improving the performance of the Python interpreter. The way PyPy improves performance is by automatically compiling slow sections of the code at runtime.

PyPy is written in a special language called RPython (rather than C) that allows developers to quickly and reliably implement advanced features and improvements. RPython means Restricted Python because it implements a restricted subset of the Python language targeted to the compiler development.

As of today, PyPy version 5.6 supports a lot of Python features and is a possible choice for a large variety of applications.

PyPy compiles code using a very clever strategy, called tracing JIT compilation. At first, the code is executed normally using interpreter calls. PyPy then starts to profile the code and identifies the most intensive loops. After the identification takes place, the compiler then observes (traces...

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